Appel: ESWC 2014, Call for Challenge, Linked Open Data-enabled Recommender Systems

[Thierry Hamon]

Date: Tue,  3 Dec 2013 09:24:36 +0100 (CET)
From: speroni at cs.unibo.it
Message-Id: <20131203082456.E1E05F983E at vina.cines.fr>
X-url: http://challenges.2014.eswc-conferences.org/RecSys


** apologies for cross-posting **

=== Call for Challenge: Linked Open Data-enabled Recommender Systems ===

Challenge Website: http://challenges.2014.eswc-conferences.org/RecSys
Call Web page: http://2014.eswc-conferences.org/important-dates/call-RecSys

MOTIVATION AND OBJECTIVES
People generally need more and more advanced tools that go beyond those
implementing the canonical search paradigm for seeking relevant
information. A new search paradigm is emerging, where the user
perspective is completely reversed: from finding to being
found. Recommender systems may help to support this new perspective,
because they have the effect of pushing relevant objects, selected from
a large space of possible options, to potentially interested users. To
achieve this result, recommendation techniques generally rely on data
referring to three kinds of objects: users, items and their relations.

Recent developments in the Semantic Web community offer novel strategies
to represent data about users, items and their relations that might
improve the current state of the art of recommender systems, in order to
move towards a new generation of recommender systems which fully
understand the items they deal with.
More and more semantic data are published following the Linked Data
principles, that enable to set up links between objects in different
data sources, by connecting information in a single global data space:
the Web of Data. Today, the Web of Data includes different types of
knowledge represented in a homogeneous form: sedimentary one
(encyclopedic, cultural, linguistic, common-sense) and real-time one
(news, data streams, ...). These data might be useful to interlink
diverse information about users, items, and their relations and
implement reasoning mechanisms that can support and improve the
recommendation process.
The primary goal of this challenge is twofold. On the one hand, we want
to create a link between the Semantic Web and the Recommender Systems
communities. On the other hand, we aim to show how Linked Open Data and
semantic technologies can boost the creation of a new breed of
knowledge-enabled and content-based recommender systems.

TARGET AUDIENCE
The target audience is all of those communities, both academic and
industrial, which are interested in personalized information access with
a particular emphasis on Linked Open Data.
During the last ACM RecSys conference more than 60% of participants were
from industry. This is for sure a witness of the actual interest of
recommender systems for industrial applications ready to be released in
the market.

TASKS
Task 1: Rating prediction in cold-start situations
This task deals with the rating prediction problem, in which a system is
requested to estimate the value of unknown numeric scores
(a.k.a. ratings) that a target user would assign to available items,
indicating whether she likes or dislikes them.
In order to favor the proposal of content-based, LOD-enabled
recommendation approaches, and limit the use of collaborative filtering
approaches, this task aims at predicting ratings in cold-start
situations, that is, predicting ratings for users who have a few past
ratings, and predicting ratings of items that have been rated by a few
users.
The dataset to use in the task - DBbook - relates to the book domain. It
contains explicit numeric ratings assigned by users to books. For each
book we provide the corresponding DBpedia URI.
Participants will have to exploit the provided ratings as training sets,
and will have to estimate unknown ratings in a non-provided evaluation
set.
Recommendation approaches will be evaluated on the evaluation set by
means of metrics that measure the differences between real and estimated
ratings, namely the Root Mean Square Error (RMSE).

Task 2: Top-N recommendation from binary user feedback
This task deals with the top-N recommendation problem, in which a system
is requested to find and recommend a limited set of N items that best
match a user profile, instead of correctly predict the ratings for all
available items.
Similarly to Task 1, in order to favor the proposal of content-based,
LOD-enabled recommendation approaches, and limit the use of
collaborative filtering approaches, this task aims at generating ranked
lists of items for which no graded ratings are available, but only
binary ones.
Also in this case, the DBbook dataset will be provided.
In this task, the accuracy of recommendation approaches will be
evaluated on an evaluation set using the F-measure.

Task 3: Diversity
A very interesting aspect of content-based recommender systems and then
of LOD-enabled ones is giving the possibility to evaluate the diversity
of recommended items in a straight way. This is a very popular topic in
content-based recommender systems, which usually suffer from
over-specialization.
In this task, the evaluation will be made by considering a combination
of both accuracy of the recommendation list and the diversity of items
belonging to it. Also for this task, DBbook dataset will be used.
Given the domain of books, diversity with respect to the two properties
dbpedia-owl:author and skos:subject will be considered.

EVALUATION DATASET AND EVALUATION METRICS
DBbook dataset
This dataset relies on user data and preferences retrieved from the
Web. The books available in the dataset have been mapped to their
corresponding DBpedia URIs. The mapping contains 8170 DBpedia URIs.
These mappings can be used to extract semantic features from DBpedia or
other LOD repositories to be exploited by the recommendation approaches
proposed in the challenge.
The dataset is split in a training set and an evaluation set. In the
former, user ratings are provided to train a system while in the latter,
ratings have been removed, and they will be used in the eventual
evaluation step.

Participants will generate the ratings (Task 1) or the ranking (Task 2
and Task 3) for the test set, and their results will be compared and
evaluated with respect to actual users ratings (hidden evaluation data).

The two sets will be provided at the challenge's website, in the following plain text files:
* lodrecsys2014-DBbook-training-ratings.txt: Contains tuples <user,
book, rating> that may be used to build the recommendation approaches. 
* lodrecsys2014-DBbook-test-ratings.txt: Contains tuples <user, book>
that will be used to evaluate the recommendation approaches. 

Together with the two previous files, a further one will be provided
containing the mapping to DBpedia.
* lodrecsys2014-DBbook-mappings.txt: Contains tuples <book, bookURI>
associated with the books in DBbook that will be used to extract and
exploit semantic features from DBpedia and other LOD repositories.

Recommendation approaches will be evaluated with the metrics requested
in each task. We provide a number of Java classes that compute the
different metrics and a Web Service to test intermediate results. A
description of the metrics to compute and further details regarding the
evaluation will be available at the challenge website.


JUDGING AND PRIZES
After a first round of reviews, the Program Committee and the chairs
will select a number of submissions that will have to satisfy the
challenge's requirements, and will have to be presented at the
conference. Submissions accepted for presentation will receive
constructive reviews from the Program Committee, and will be included in
post-proceedings. All accepted submissions will have a slot in a poster
session dedicated to the challenge. In addition, the winners will
present their work in a special slot of the main program of ESWC'14, and
will be invited to submit a paper to a dedicated Semantic Web Journal
special issue.

For each task we will select:
* the best performing tool, given to the paper which will get the
  highest score in the evaluation
* the most original approach, selected by the Challenge Program
  Committee with the reviewing process

HOW TO PARTICIPATE
The following information has to be provided:
* Abstract: no more than 200 words.
* Description: It should contain the details of the system, including
  why the system is innovative, how it uses Semantic Web, which features
  or functions the system provides, what design choices were made, and
  what lessons were learned. The description should also summarize how
  participants have addressed the evaluation tasks. Papers must be
  submitted in PDF format, following the style of the Springer's Lecture
  Notes in Computer Science (LNCS) series
  (http://www.springer.com/computer/lncs/lncs+authors), and not
  exceeding 5 pages in length.
* Result evaluation: For the three tasks previously described, a
  Web-accessible service will be provided in order to evaluate the
  produced results. All the details about the format and the service URL
  will be provided on the website.

All submissions should be provided via EasyChair
https://www.easychair.org/conferences/?conf=eswc2014-challenges

MAILING LIST

We invite the potential participants to subscribe to our mailing list in
order to be kept up to date with the latest news related to the
challenge.

https://lists.sti2.org/mailman/listinfo/eswc2014-recsys-challenge


IMPORTANT DATES
* March 7, 2014, 23:59 (Hawaii time): Abstract Submission
* March 14, 2014, 23:59 (Hawaii time): Submission
* April 9, 2014, 23:59 (Hawaii time): Notification of acceptance
* May 27-29, 2014: Challenge days


CHALLENGE CHAIRS
* Tommaso Di Noia (Polytechnic University of Bari, IT)
* Ivan Cantador (Universidad Autonoma de Madrid, ES)

EVALUATION COORDINATOR
* Vito Claudio Ostuni (Polytechnic University of Bari, IT)

PROGRAM COMMITTEE (to be completed)
* Oscar Corcho (Universidad Politecnica de Madrid, ES)
* Marco de Gemmis (University of Bari Aldo Moro, IT)
* Frank Hopfgartner (Technische Universitat Berlin, DE)
* Andreas Hotho (Universitat Wurzburg, DE)
* Dietmar Jannach (TU Dortmund University, DE)
* Pasquale Lops (University of Bari Aldo Moro, IT)
* Valentina Maccatrozzo (Delft University of Technology, NL)
* Francesco Ricci (Free University of Bozen-Bolzano, IT)
* Giovanni Semeraro (University of Bari Aldo Moro, IT)
* David Vallet (NICTA, AU)
* Manolis Wallace (University of Peloponnese, GR)
* Markus Zanker (Alpen-Adria-Universitaet Klagenfurt, AT)
 

ESWC CHALLENGE COORDINATOR
* Milan Stankovic (Sepage & Universite Paris-Sorbonne, FR)